def initializeEnvironment(numOfAgent):
sheepId = 0
wolfId = 1
distractorsIds = list(range(2, numOfAgent))
distanceToVisualDegreeRatio = 20
minInitSheepWolfDistance = 9 * distanceToVisualDegreeRatio
minInitSheepDistractorDistance = 2.5 * distanceToVisualDegreeRatio # no distractor in killzone when init
isLegalInitPositions = IsLegalInitPositions(sheepId, wolfId, distractorsIds, minInitSheepWolfDistance,
minInitSheepDistractorDistance)
xBoundary = [0, 640]
yBoundary = [0, 480]
resetState = ResetState(xBoundary, yBoundary, numOfAgent, isLegalInitPositions, transPolarToCartesian)
killzoneRadius = 2.5 * distanceToVisualDegreeRatio
isTerminal = IsTerminal(sheepId, wolfId, killzoneRadius)
return resetState,isTerminal
def __call__(self, parameters):
numOfAgent = parameters['numOfAgent']
trajectories = []
for trajectoryId in range(self.numTrajectories):
forwardOneStep = self.composeFowardOneTimeStepWithRandomSubtlety(
numOfAgent)
sheepId = 0
wolfId = 1
distractorsIds = list(range(2, numOfAgent))
distanceToVisualDegreeRatio = 20
minInitSheepWolfDistance = 9 * distanceToVisualDegreeRatio
minInitSheepDistractorDistance = 2.5 * distanceToVisualDegreeRatio # no distractor in killzone when init
isLegalInitPositions = IsLegalInitPositions(
sheepId, wolfId, distractorsIds, minInitSheepWolfDistance,
minInitSheepDistractorDistance)
xBoundary = [0, 600]
yBoundary = [0, 600]
resetState = ResetState(xBoundary, yBoundary, numOfAgent,
isLegalInitPositions,
transPolarToCartesian)
killzoneRadius = 2.5 * distanceToVisualDegreeRatio
isTerminal = IsTerminal(sheepId, wolfId, killzoneRadius)
numMDPTimeStepPerSecond = 5
maxRunningSteps = 25 * numMDPTimeStepPerSecond
sampleTrajecoty = SampleTrajectory(maxRunningSteps, isTerminal,
resetState, forwardOneStep)
numActionDirections = 8
actionSpace = [
(np.cos(directionId * 2 * math.pi / numActionDirections),
np.sin(directionId * 2 * math.pi / numActionDirections))
for directionId in range(numActionDirections)
]
randomPolicy = RandomPolicy(actionSpace)
sampleAction = lambda state: sampleFromDistribution(
randomPolicy(state))
trajectory = sampleTrajecoty(sampleAction)
trajectories.append(trajectory)
return trajectories
def composeFowardOneTimeStepWithRandomSubtlety(
numOfAgent
): # one time step used in different algorithems; here evaluate number of agent
# MDP
# experiment parameter for env
numMDPTimeStepPerSecond = 5 # change direction every 200ms
distanceToVisualDegreeRatio = 20
minSheepSpeed = int(17.4 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
maxSheepSpeed = int(23.2 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
warmUpTimeSteps = 10 * numMDPTimeStepPerSecond # 10s to warm up
prepareSheepVelocity = PrepareSheepVelocity(minSheepSpeed, maxSheepSpeed,
warmUpTimeSteps)
minWolfSpeed = int(8.7 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
maxWolfSpeed = int(14.5 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
# wolfSubtleties = [500, 11, 3.3, 1.83, 0.92, 0.31, 0.001]
wolfSubtleties = [500] # 0, 30, 60, .. 180
initWolfSubtlety = np.random.choice(wolfSubtleties)
prepareWolfVelocity = PrepareWolfVelocity(minWolfSpeed, maxWolfSpeed,
warmUpTimeSteps,
initWolfSubtlety,
transCartesianToPolar,
transPolarToCartesian)
minDistractorSpeed = int(8.7 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
maxDistractorSpeed = int(14.5 * distanceToVisualDegreeRatio /
numMDPTimeStepPerSecond)
prepareDistractorVelocity = PrepareDistractorVelocity(
minDistractorSpeed, maxDistractorSpeed, warmUpTimeSteps,
transCartesianToPolar, transPolarToCartesian)
sheepId = 0
wolfId = 1
distractorsIds = list(range(2, numOfAgent))
prepareAllAgentsVelocities = PrepareAllAgentsVelocities(
sheepId, wolfId, distractorsIds, prepareSheepVelocity,
prepareWolfVelocity, prepareDistractorVelocity)
xBoundary = [0, 640]
yBoundary = [0, 480]
stayInBoundaryByReflectVelocity = StayInBoundaryByReflectVelocity(
xBoundary, yBoundary)
killzoneRadius = 2.5 * distanceToVisualDegreeRatio
isTerminal = IsTerminal(sheepId, wolfId, killzoneRadius)
numFramePerSecond = 30 # visual display fps
numFramesToInterpolate = int(
numFramePerSecond / numMDPTimeStepPerSecond - 1
) # interpolate each MDP timestep to multiple frames; check terminal for each frame
transitFunction = TransitWithInterpolation(
initWolfSubtlety, numFramesToInterpolate, prepareAllAgentsVelocities,
stayInBoundaryByReflectVelocity, isTerminal)
aliveBonus = 0.01
deathPenalty = -1
rewardFunction = RewardFunctionTerminalPenalty(aliveBonus, deathPenalty,
isTerminal)
forwardOneStep = ForwardOneStep(transitFunction, rewardFunction)
minInitSheepWolfDistance = 9 * distanceToVisualDegreeRatio
minInitSheepDistractorDistance = 2.5 * distanceToVisualDegreeRatio # no distractor in killzone when init
isLegalInitPositions = IsLegalInitPositions(
sheepId, wolfId, distractorsIds, minInitSheepWolfDistance,
minInitSheepDistractorDistance)
resetState = ResetState(xBoundary, yBoundary, numOfAgent,
isLegalInitPositions, transPolarToCartesian)
return forwardOneStep, resetState, isTerminal